Halasya Siva Subramania

Ontology-driven data collection and validation framework for the diagnosis of vehicle healthmanagement

The current warranty data collection processes exhibit several data quality issues – the level of detail and precision is missing in the collected data, the semantic heterogeneity is observed and no systematic data quality validation mechanism to automatically certify the data quality. Such data cannot be translated seamlessly into the knowledge assets to perform business functions, for example, fault diagnosis. An ontology-driven structured data collection framework is proposed to acquire the necessary data in the warranty domain. The proposed framework uses the integrated vehicle health management ontology as an information model to populate necessary data acquisition fields of the framework. A novel three-dimensional data quality metric is proposed to validate the completeness, correctness and relevance of newly collected data. We also evaluate the performance of the tool by using the real-life data. The data accuracy precision after using the framework has been improved from 0.30 to 0.80, whereas the recall is improved from 0.28 to 0.70. Furthermore, the precision and recall of the tool is evaluated for the 500 real-life field failure cases and it was greater than 90% for data completeness and relevance. Throughout this paper we will use the words ‘correctness’ and ‘accuracy’ interchangeably.

Decision Forest for Root Cause Analysis of Intermittent Faults

Intermittent failures can be problematic in electronic control units (ECUs) such as engine/transmission control modules. When an ECU exhibits an internal performance fault, the ECU may malfunction, while the fault condition is active, and later, it may once again give correct results when conditions change. Due to highly unpredictable nature of intermittent faults, it can be extremely difficult to diagnose them. Therefore, there is a need to enhance the fault diagnosis of intermittent faults in ECUs. In this paper, we propose an off-board, data-driven approach that can assist diagnostic engineers to investigate intermittent faults using fleet-wide field failure data. The field failure data may include a large number of intermittent faults and concomitant operating parameters (e.g., vehicle speed, engine speed, control module voltage, powertrain relay voltage, etc.) recorded at the time when the faults occurred. We describe a decision forest method to identify a reduced set of informative operating parameters, i.e., features that separate or characterize the operating conditions of the intermittent fault from baseline, i.e., classes in feature selection space. A web-based application has been developed to assist the diagnostic engineers. We demonstrate the capabilities of our method using three case studies for an automobile test fleet.